




'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 	_snake_case:
 __snake_case:							CommonSchedulerState

 # setable values
 __snake_case:							jnp.ndarray
 __snake_case:							jnp.ndarray
 __snake_case:							Optional[int]						=					None






 @classmethod
 def 						_UpperCamelCase (cls       :  int    ,		a       :  CommonSchedulerState    ,		a       :  jnp.ndarray    ,		a       :  jnp.ndarray    )       ->   Dict:






    """simple docstring"""


    return cls(common=a    ,		init_noise_sigma=a    ,		timesteps=a    )




@dataclass
class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							DDPMSchedulerState




class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase      ):
 __snake_case:							Union[str, Any]						=					[e.name for e in FlaxKarrasDiffusionSchedulers]

 __snake_case:							jnp.dtype






 @property
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Optional[int]:






    """simple docstring"""


    return True






 @register_to_config
 def __init__(self       :  Tuple    ,		a       :  int = 10_00    ,		a       :  float = 0.0001    ,		a       :  float = 0.02    ,		a       :  str = "linear"    ,		a       :  Optional[jnp.ndarray] = None    ,		a       :  str = "fixed_small"    ,		a       :  bool = True    ,		a       :  str = "epsilon"    ,		a       :  jnp.dtype = jnp.floataa    ,		)       ->   List[str]:






    """simple docstring"""


    A__       =     dtype






 def 						_UpperCamelCase (self       :  Optional[Any]    ,		a       :  Optional[CommonSchedulerState] = None    )       ->   DDPMSchedulerState:






    """simple docstring"""


    if common is None:
       A__       =     CommonSchedulerState.create(self    )

    # standard deviation of the initial noise distribution
    A__       =     jnp.array(1.0    ,		dtype=self.dtype    )

    A__       =     jnp.arange(0    ,		self.config.num_train_timesteps    ).round()[::-1]

    return DDPMSchedulerState.create(
        common=a    ,		init_noise_sigma=a    ,		timesteps=a    ,		)






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  DDPMSchedulerState    ,		a       :  jnp.ndarray    ,		a       :  Optional[int] = None    )       ->   jnp.ndarray:






    """simple docstring"""


    return sample






 def 						_UpperCamelCase (self       :  str    ,		a       :  DDPMSchedulerState    ,		a       :  int    ,		a       :  Tuple = ()    )       ->   DDPMSchedulerState:






    """simple docstring"""


    A__       =     self.config.num_train_timesteps // num_inference_steps
    # creates integer timesteps by multiplying by ratio
    # rounding to avoid issues when num_inference_step is power of 3
    A__       =     (jnp.arange(0    ,		a    ) * step_ratio).round()[::-1]

    return state.replace(
        num_inference_steps=a    ,		timesteps=a    ,		)






 def 						_UpperCamelCase (self       :  Any    ,		a       :  DDPMSchedulerState    ,		a       :  Dict    ,		a       :  int=None    ,		a       :  Dict=None    )       ->   Dict:






    """simple docstring"""


    A__       =     state.common.alphas_cumprod[t]
    A__       =     jnp.where(t > 0    ,		state.common.alphas_cumprod[t - 1]    ,		jnp.array(1.0    ,		dtype=self.dtype    )    )

    # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
    # and sample from it to get previous sample
    # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
    A__       =     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

    if variance_type is None:
       A__       =     self.config.variance_type

    # hacks - were probably added for training stability
    if variance_type == "fixed_small":
       A__       =     jnp.clip(a    ,		a_min=1e-20    )
    # for rl-diffuser https://arxiv.org/abs/2205.09991
    elif variance_type == "fixed_small_log":
       A__       =     jnp.log(jnp.clip(a    ,		a_min=1e-20    )    )
    elif variance_type == "fixed_large":
       A__       =     state.common.betas[t]
    elif variance_type == "fixed_large_log":
       # Glide max_log
       A__       =     jnp.log(state.common.betas[t]    )
    elif variance_type == "learned":
       return predicted_variance
    elif variance_type == "learned_range":
       A__       =     variance
       A__       =     state.common.betas[t]
       A__       =     (predicted_variance + 1) / 2
       A__       =     frac * max_log + (1 - frac) * min_log

    return variance






 def 						_UpperCamelCase (self       :  Optional[Any]    ,		a       :  DDPMSchedulerState    ,		a       :  jnp.ndarray    ,		a       :  int    ,		a       :  jnp.ndarray    ,		a       :  Optional[jax.random.KeyArray] = None    ,		a       :  bool = True    ,		)       ->   Union[FlaxDDPMSchedulerOutput, Tuple]:






    """simple docstring"""


    A__       =     timestep

    if key is None:
       A__       =     jax.random.PRNGKey(0    )

    if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
       A__  ,			A__       =     jnp.split(a    ,		sample.shape[1]    ,		axis=1    )
    else:
       A__       =     None

    # 1. compute alphas, betas
    A__       =     state.common.alphas_cumprod[t]
    A__       =     jnp.where(t > 0    ,		state.common.alphas_cumprod[t - 1]    ,		jnp.array(1.0    ,		dtype=self.dtype    )    )
    A__       =     1 - alpha_prod_t
    A__       =     1 - alpha_prod_t_prev

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if self.config.prediction_type == "epsilon":
       A__       =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif self.config.prediction_type == "sample":
       A__       =     model_output
    elif self.config.prediction_type == "v_prediction":
       A__       =     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
    else:
       raise ValueError(
           f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
           ' for the FlaxDDPMScheduler.'    )

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
       A__       =     jnp.clip(a    ,		-1    ,		1    )

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    A__       =     (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
    A__       =     state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    A__       =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    def random_variance():
       A__       =     jax.random.split(a    ,		num=1    )
       A__       =     jax.random.normal(a    ,		shape=model_output.shape    ,		dtype=self.dtype    )
       return (self._get_variance(a    ,		a    ,		predicted_variance=a    ) ** 0.5) * noise

    A__       =     jnp.where(t > 0    ,		random_variance()    ,		jnp.zeros(model_output.shape    ,		dtype=self.dtype    )    )

    A__       =     pred_prev_sample + variance

    if not return_dict:
       return (pred_prev_sample, state)

    return FlaxDDPMSchedulerOutput(prev_sample=a    ,		state=a    )






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  DDPMSchedulerState    ,		a       :  jnp.ndarray    ,		a       :  jnp.ndarray    ,		a       :  jnp.ndarray    ,		)       ->   jnp.ndarray:






    """simple docstring"""


    return add_noise_common(state.common    ,		a    ,		a    ,		a    )






 def 						_UpperCamelCase (self       :  Union[str, Any]    ,		a       :  DDPMSchedulerState    ,		a       :  jnp.ndarray    ,		a       :  jnp.ndarray    ,		a       :  jnp.ndarray    ,		)       ->   jnp.ndarray:






    """simple docstring"""


    return get_velocity_common(state.common    ,		a    ,		a    ,		a    )






 def __len__(self       :  Optional[int]    )       ->   int:






    """simple docstring"""


    return self.config.num_train_timesteps








'''simple docstring'''







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase_     						=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if not path:
      return "pipe"

   for ext in PipelineDataFormat.SUPPORTED_FORMATS:
      if path.endswith(UpperCAmelCase						):
         return ext

   raise Exception(
       F"""Unable to determine file format from file extension {path}. """
       F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     pipeline(
       task=args.task      ,model=args.model if args.model else None      ,config=args.config      ,tokenizer=args.tokenizer      ,device=args.device      ,)
   A__       =     try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
   A__       =     PipelineDataFormat.from_str(
       format=UpperCAmelCase      ,output_path=args.output      ,input_path=args.input      ,column=args.column if args.column else nlp.default_input_names      ,overwrite=args.overwrite      ,)
   return RunCommand(UpperCAmelCase      ,UpperCAmelCase						)







class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Tuple    ,		a       :  Pipeline    ,		a       :  PipelineDataFormat    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     nlp
    A__       =     reader






 @staticmethod
 def 						_UpperCamelCase (a       :  ArgumentParser    )       ->   str:






    """simple docstring"""


    A__       =     parser.add_parser('run'    ,		help='Run a pipeline through the CLI'    )
    run_parser.add_argument('--task'    ,		choices=get_supported_tasks()    ,		help='Task to run'    )
    run_parser.add_argument('--input'    ,		type=a    ,		help='Path to the file to use for inference'    )
    run_parser.add_argument('--output'    ,		type=a    ,		help='Path to the file that will be used post to write results.'    )
    run_parser.add_argument('--model'    ,		type=a    ,		help='Name or path to the model to instantiate.'    )
    run_parser.add_argument('--config'    ,		type=a    ,		help='Name or path to the model\'s config to instantiate.'    )
    run_parser.add_argument(
        '--tokenizer'    ,		type=a    ,		help='Name of the tokenizer to use. (default: same as the model name)'    )
    run_parser.add_argument(
        '--column'    ,		type=a    ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'    ,		)
    run_parser.add_argument(
        '--format'    ,		type=a    ,		default='infer'    ,		choices=PipelineDataFormat.SUPPORTED_FORMATS    ,		help='Input format to read from'    ,		)
    run_parser.add_argument(
        '--device'    ,		type=a    ,		default=-1    ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'    ,		)
    run_parser.add_argument('--overwrite'    ,		action='store_true'    ,		help='Allow overwriting the output file.'    )
    run_parser.set_defaults(func=a    )






 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    A__  ,			A__       =     self._nlp, []

    for entry in self._reader:
       A__       =     nlp(**a    ) if self._reader.is_multi_columns else nlp(a    )
       if isinstance(a    ,		a    ):
          outputs.append(a    )
       else:
          outputs += output

        # Saving data
    if self._nlp.binary_output:
       A__       =     self._reader.save_binary(a    )
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""    )
    else:
       self._reader.save(a    )


